Learn TensorFlow and deep learning, without a Ph.D.

To help more developers embrace deep-learning techniques, without the need to earn a Ph.D., I have attempted to flatten the learning curve by building a short crash-course (3 hours total). The course is focused on a few basic network architectures, including dense, convolutional and recurrent networks, and training techniques such as dropout or batch normalization. (This course was initially presented at the Devoxx conference in Antwerp, Belgium, in November 2016.) By watching the recordings of the course and viewing the annotated slides, you can learn how to solve a couple of typical problems with neural networks and also pick up enough vocabulary and concepts to continue your deep learning self-education — for example, by exploring TensorFlow resources. (TensorFlow is Google’s internally developed framework for deep learning, which has been growing in popularity since it was released as open source in 2015.)

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